Prediction of Clinical Drug Efficacy by Classification of Drug-Induced Genomic Expression Profiles in vitro

Assays of drug action typically evaluate biochemical activity. However, accurately matching therapeutic efficacy with biochemical activity is a challenge. High-content cellular assays seek to bridge this gap by capturing broad information about the cellular physiology of drug action. Here, we presen...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2003-08, Vol.100 (16), p.9608-9613
Main Authors: Gunther, Erik C., Stone, David J., Gerwien, Robert W., Bento, Patricia, Heyes, Melvyn P.
Format: Article
Language:English
Subjects:
Algorithms
Antidepressants
Antipsychotic agents
Biological markers
Biological Sciences
Clinical outcomes
Cluster Analysis
Databases, Genetic
Datasets
DNA, Complementary - metabolism
Drug Design
Drug Evaluation, Preclinical - methods
Gene Expression
Genes
Humans
Medical treatment
Models, Statistical
Neurons - metabolism
Oligonucleotide Array Sequence Analysis
Opioid receptors
Pharmaceuticals
Pharmacogenetics - methods
Pharmacology
Serotonin agents
We they distinction
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Summary:Assays of drug action typically evaluate biochemical activity. However, accurately matching therapeutic efficacy with biochemical activity is a challenge. High-content cellular assays seek to bridge this gap by capturing broad information about the cellular physiology of drug action. Here, we present a method of predicting the general therapeutic classes into which various psychoactive drugs fall, based on high-content statistical categorization of gene expression profiles induced by these drugs. When we used the classification tree and random forest supervised classification algorithms to analyze microarray data, we derived general "efficacy profiles" of biomarker gene expression that correlate with anti-depressant, antipsychotic and opioid drug action on primary human neurons in vitro. These profiles were used as predictive models to classify naïve in vitro drug treatments with 83.3% (random forest) and 88.9% (classification tree) accuracy. Thus, the detailed information contained in genomic expression data is sufficient to match the physiological effect of a novel drug at the cellular level with its clinical relevance. This capacity to identify therapeutic efficacy on the basis of gene expression signatures in vitro has potential utility in drug discovery and drug target validation.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1632587100